{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 训练数据和测试数据分割（随机选择20%的数据作为测试集）；（10分）\n",
    "2. 适当的特征工程（及数据探索）;（10分）\n",
    "3. Logistic回归，并选择最佳的正则函数（L1/L2）及正则参数；（30分）\n",
    "4. 线性SVM，并选择最佳正则参数，比较与Logistic回归的性能，简单说明原因。（20分）\n",
    "5. RBF核的SVM，并选择最佳的超参数（正则参数、RBF核函数宽度）；（30分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pyplot\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据类型信息\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "print(\"数据类型信息\")\n",
    "data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据分布信息\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print(data.head(1))\n",
    "print(\"数据分布信息\")\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pregnancies' 'Glucose' 'BloodPressure' 'SkinThickness' 'Insulin' 'BMI'\n",
      " 'DiabetesPedigreeFunction' 'Age' 'Outcome']\n",
      "【zero】统计: Pregnancies 111\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeYAAAFXCAYAAAB3Be0fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmAEAsAjBDACARQhmAAAsQjADAGCR/wc1RYd1yFksCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ac2d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【zero】统计: Glucose 5\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFXCAYAAABz8D0iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkAEAMIAgAwBgwP8D2l9h6vkfzKYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115cc2f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【zero】统计: BloodPressure 35\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFXCAYAAABz8D0iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAIZAAADEMgAABiAQAYAwAD/D+SzEoYtOOFbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d8a350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【zero】统计: SkinThickness 227\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFXCAYAAABz8D0iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBBkAAAMIMgAABhBkAAAM+D8wlUt+S30+vAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1190b3450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【zero】统计: Insulin 374\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFXCAYAAABz8D0iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGgsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118e6bad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【zero】统计: BMI 11\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFXCAYAAACYx4YhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115bda7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【zero】统计: DiabetesPedigreeFunction 0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeUAAAFXCAYAAACcMlYcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115bf7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【zero】统计: Age 0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFXCAYAAABz8D0iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5z8d0Ju9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1192dd050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【zero】统计: Outcome 500\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFXCAYAAABz8D0iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGMdJREFUeJzt3X9s1PUdx/HX9ccVba8CWTUTV2Y7qkMo7dbAZkMFgdVl\nskgRCqeVTKeDuJlWdIBCyxZQC6SbkpHJwrakE0q1xADZD0eFNSmumUUgI9aFqs1gAzss8+7Aq6Xf\n/bHQycQ79b7tva99Pv7r/fjc5/uW+Ox9Ob7ncRzHEQAAiKukeG8AAAAQZAAATCDIAAAYQJABADCA\nIAMAYABBBgDAgJR4vnh3d8DV9caMuVI9PedcXXMkYo6xY4axY4axY4axc3uGWVm+j71vWL1DTklJ\njvcWhgXmGDtmGDtmGDtmGLuhnOGwCjIAAImKIAMAYABBBgDAAIIMAIABBBkAAAMIMgAABhBkAAAM\nIMgAABhAkAEAMOATXTpz3rx5ysjIkCRdd911Wrp0qVauXCmPx6MJEyaopqZGSUlJamxsVENDg1JS\nUrRs2TLNnDlzUDcPAMBwETXI4XBYjuOovr5+4LalS5eqsrJS06ZNU3V1tZqbm1VQUKD6+no1NTUp\nHA7L7/eruLhYXq93UA8AAIDhIGqQOzo6dP78ed17773q6+vTww8/rGPHjmnq1KmSpJKSErW2tiop\nKUmFhYXyer3yer3Kzs5WR0eH8vPzB/0gAABIdFGDPGrUKN13331asGCB3n77bd1///1yHEcej0eS\nlJ6erkAgoGAwKJ/vf99ikZ6ermAwGHHtMWOudPXC3b9/5W3X1rrotq9/0fU1E0GkbyTBJ8MMY8cM\nY8cMYzdUM4wa5Ouvv17jx4+Xx+PR9ddfr9GjR+vYsWMD94dCIWVmZiojI0OhUOiS2z8c6MsZjK8F\nCwTfd3U9t78iMhFkZflG5HG7iRnGjhnGjhnGzu0ZxvT1iy+88IKeeuopSdLp06cVDAZVXFystrY2\nSVJLS4uKioqUn5+v9vZ2hcNhBQIBdXZ2Ki8vz6VDAABgeIv6DvnOO+/UqlWrtHjxYnk8Hj3xxBMa\nM2aM1qxZo7q6OuXk5Ki0tFTJycmqqKiQ3++X4ziqqqpSWlraUBwDAAAJz+M4jhOvF3f7VEr78TOu\nn7KeUTDO1fUSAae5YscMY8cMY8cMY2fqlDUAABh8BBkAAAMIMgAABhBkAAAMIMgAABhAkAEAMIAg\nAwBgAEEGAMAAggwAgAEEGQAAAwgyAAAGEGQAAAwgyAAAGECQAQAwgCADAGAAQQYAwACCDACAAQQZ\nAAADCDIAAAYQZAAADCDIAAAYQJABADCAIAMAYABBBgDAAIIMAIABBBkAAAMIMgAABhBkAAAMIMgA\nABhAkAEAMIAgAwBgAEEGAMAAggwAgAEEGQAAAwgyAAAGEGQAAAwgyAAAGECQAQAwgCADAGAAQQYA\nwACCDACAAQQZAAADCDIAAAYQZAAADCDIAAAYQJABADCAIAMAYABBBgDAAIIMAIABnyjIZ86c0S23\n3KLOzk51dXVp8eLF8vv9qqmpUX9/vySpsbFRZWVlWrhwofbv3z+omwYAYLiJGuQPPvhA1dXVGjVq\nlCTpySefVGVlpbZv3y7HcdTc3Kzu7m7V19eroaFB27ZtU11dnXp7ewd98wAADBdRg1xbW6tFixbp\n6quvliQdO3ZMU6dOlSSVlJTo4MGDOnr0qAoLC+X1euXz+ZSdna2Ojo7B3TkAAMNISqQ7d+3apbFj\nx2r69OnaunWrJMlxHHk8HklSenq6AoGAgsGgfD7fwPPS09MVDAajvviYMVcqJSU5lv1f6vgZ+TJG\nubeepKwsX/QHDUMj9bjdxAxjxwxjxwxjN1QzjBjkpqYmeTwevfLKK3r99de1YsUKvfvuuwP3h0Ih\nZWZmKiMjQ6FQ6JLbPxzoj9PTcy6GrV9eIPi+q+t1dwdcXS8RZGX5RuRxu4kZxo4Zxo4Zxs7tGUaK\ne8RT1s8995x+85vfqL6+Xl/+8pdVW1urkpIStbW1SZJaWlpUVFSk/Px8tbe3KxwOKxAIqLOzU3l5\nea4dAAAAw13Ed8iXs2LFCq1Zs0Z1dXXKyclRaWmpkpOTVVFRIb/fL8dxVFVVpbS0tMHYLwAAw5LH\ncRwnXi/u9qmU9uNnXD9lPaNgnKvrJQJOc8WOGcaOGcaOGcbOzClrAAAwNAgyAAAGEGQAAAwgyAAA\nGECQAQAwgCADAGAAQQYAwACCDACAAQQZAAADCDIAAAYQZAAADCDIAAAYQJABADCAIAMAYABBBgDA\nAIIMAIABBBkAAAMIMgAABhBkAAAMIMgAABhAkAEAMIAgAwBgAEEGAMAAggwAgAEEGQAAAwgyAAAG\nEGQAAAwgyAAAGECQAQAwgCADAGAAQQYAwACCDACAAQQZAAADCDIAAAYQZAAADCDIAAAYQJABADCA\nIAMAYABBBgDAAIIMAIABBBkAAAMIMgAABhBkAAAMIMgAABhAkAEAMIAgAwBgAEEGAMAAggwAgAEE\nGQAAA1KiPeDChQtavXq13nrrLXk8Hv3oRz9SWlqaVq5cKY/HowkTJqimpkZJSUlqbGxUQ0ODUlJS\ntGzZMs2cOXMojgEAgIQXNcj79++XJDU0NKitrU0/+clP5DiOKisrNW3aNFVXV6u5uVkFBQWqr69X\nU1OTwuGw/H6/iouL5fV6B/0gAABIdFGDPHv2bM2YMUOS9I9//EOZmZk6ePCgpk6dKkkqKSlRa2ur\nkpKSVFhYKK/XK6/Xq+zsbHV0dCg/P39QDwAAgOEgapAlKSUlRStWrNAf//hHPfPMM2ptbZXH45Ek\npaenKxAIKBgMyufzDTwnPT1dwWAw4rpjxlyplJTkGLb/f46fkS9jlHvrScrK8kV/0DA0Uo/bTcww\ndswwdswwdkM1w08UZEmqra3VI488ooULFyocDg/cHgqFlJmZqYyMDIVCoUtu/3CgL6en59xn2HJk\ngeD7rq7X3R1wdb1EkJXlG5HH7SZmGDtmGDtmGDu3Zxgp7lE/Zf3iiy/q2WeflSRdccUV8ng8mjRp\nktra2iRJLS0tKioqUn5+vtrb2xUOhxUIBNTZ2am8vDyXDgEAgOEt6jvkb3zjG1q1apXuuusu9fX1\n6bHHHlNubq7WrFmjuro65eTkqLS0VMnJyaqoqJDf75fjOKqqqlJaWtpQHAMAAAnP4ziOE68Xd/tU\nSvvxM66fsp5RMM7V9RIBp7lixwxjxwxjxwxjZ+qUNQAAGHwEGQAAAwgyAAAGEGQAAAwgyAAAGECQ\nAQAwgCADAGAAQQYAwACCDACAAQQZAAADCDIAAAYQZAAADCDIAAAYEPXrFwEASBQHDp90db0Fc250\ndb1IeIcMAIABBBkAAAMIMgAABhBkAAAMIMgAABhAkAEAMIAgAwBgAEEGAMAAggwAgAEEGQAAAwgy\nAAAGEGQAAAwgyAAAGECQAQAwgCADAGAAQQYAwACCDACAAQQZAAADCDIAAAYQZAAADCDIAAAYQJAB\nADCAIAMAYABBBgDAAIIMAIABBBkAAAMIMgAABhBkAAAMIMgAABhAkAEAMIAgAwBgAEEGAMAAggwA\ngAEEGQAAAwgyAAAGpES684MPPtBjjz2mkydPqre3V8uWLdOXvvQlrVy5Uh6PRxMmTFBNTY2SkpLU\n2NiohoYGpaSkaNmyZZo5c+ZQHQMAAAkvYpB3796t0aNHa+PGjTp79qzuuOMO3XjjjaqsrNS0adNU\nXV2t5uZmFRQUqL6+Xk1NTQqHw/L7/SouLpbX6x2q4wAAIKFFDPJtt92m0tJSSZLjOEpOTtaxY8c0\ndepUSVJJSYlaW1uVlJSkwsJCeb1eeb1eZWdnq6OjQ/n5+YN/BAAADAMRg5yeni5JCgaDeuihh1RZ\nWana2lp5PJ6B+wOBgILBoHw+3yXPCwaDUV98zJgrlZKSHMv+L3X8jHwZo9xbT1JWli/6g4ahkXrc\nbmKGsWOGsRtpM3S7AdLQzTBikCXpn//8px588EH5/X7NnTtXGzduHLgvFAopMzNTGRkZCoVCl9z+\n4UB/nJ6ec59x2x8vEHzf1fW6uwOurpcIsrJ8I/K43cQMY8cMYzcSZ+h2AyR3OxAp7hE/Zf2vf/1L\n9957rx599FHdeeedkqSJEyeqra1NktTS0qKioiLl5+ervb1d4XBYgUBAnZ2dysvLc+0AAAAY7iK+\nQ/75z3+u9957T1u2bNGWLVskSY8//rjWrVunuro65eTkqLS0VMnJyaqoqJDf75fjOKqqqlJaWtqQ\nHAAAAMOBx3EcJ14v7vaplPbjZ1w/XTGjYJyr6yWCkXiay23MMHbMMHYjcYYHDp90db0Fc260ccoa\nAAAMDYIMAIABBBkAAAMIMgAABhBkAAAMIMgAABhAkAEAMIAgAwBgAEEGAMAAggwAgAEEGQAAAwgy\nAAAGEGQAAAwgyAAAGECQAQAwgCADAGAAQQYAwACCDACAAQQZAAADCDIAAAYQZAAADCDIAAAYQJAB\nADCAIAMAYABBBgDAAIIMAIABBBkAAAMIMgAABhBkAAAMIMgAABhAkAEAMIAgAwBgAEEGAMAAggwA\ngAEEGQAAAwgyAAAGEGQAAAwgyAAAGECQAQAwgCADAGAAQQYAwACCDACAAQQZAAADCDIAAAYQZAAA\nDCDIAAAYQJABADCAIAMAYABBBgDAgE8U5CNHjqiiokKS1NXVpcWLF8vv96umpkb9/f2SpMbGRpWV\nlWnhwoXav3//4O0YAIBhKGqQf/GLX2j16tUKh8OSpCeffFKVlZXavn27HMdRc3Ozuru7VV9fr4aG\nBm3btk11dXXq7e0d9M0DADBcRA1ydna2Nm/ePPDzsWPHNHXqVElSSUmJDh48qKNHj6qwsFBer1c+\nn0/Z2dnq6OgYvF0DADDMpER7QGlpqU6cODHws+M48ng8kqT09HQFAgEFg0H5fL6Bx6SnpysYDEZ9\n8TFjrlRKSvJn2fflHT8jX8Yo99aTlJXli/6gYWikHrebmGHsmGHsRtoM3W6ANHQzjBrk/5eU9L83\n1aFQSJmZmcrIyFAoFLrk9g8H+uP09Jz7tC8fVSD4vqvrdXcHXF0vEWRl+UbkcbuJGcaOGcZuJM7Q\n7QZI7nYgUtw/9aesJ06cqLa2NklSS0uLioqKlJ+fr/b2doXDYQUCAXV2diovL++z7xgAgBHmU79D\nXrFihdasWaO6ujrl5OSotLRUycnJqqiokN/vl+M4qqqqUlpa2mDsFwCAYcnjOI4Trxd3+1RK+/Ez\nrp+umFEwztX1EsFIPM3lNmYYO2YYu5E4wwOHT7q63oI5N9o9ZQ0AANxHkAEAMIAgAwBgAEEGAMAA\nggwAgAEEGQAAAwgyAAAGEGQAAAwgyAAAGECQAQAwgCADAGAAQQYAwACCDACAAQQZAAADCDIAAAYQ\nZAAADCDIAAAYQJABADCAIAMAYABBBgDAAIIMAIABBBkAAAMIMgAABhBkAAAMIMgAABhAkAEAMIAg\nAwBgAEEGAMAAggwAgAEEGQAAAwgyAAAGEGQAAAwgyAAAGECQAQAwgCADAGAAQQYAwACCDACAAQQZ\nAAADCDIAAAYQZAAADCDIAAAYQJABADCAIAMAYABBBgDAAIIMAIABBBkAAAMIMgAABhBkAAAMIMgA\nABiQ4uZi/f39Wrt2rd544w15vV6tW7dO48ePd/MlAAAYllx9h7xv3z719vZq586dWr58uZ566ik3\nlwcAYNhyNcjt7e2aPn26JKmgoEB//etf3VweAIBhy9VT1sFgUBkZGQM/Jycnq6+vTykpl3+ZrCyf\nmy+v21xebyRz+7/NSMQMY8cMYzfSZrhgzo2urzlUM3T1HXJGRoZCodDAz/39/R8bYwAA8D+uBvkr\nX/mKWlpaJEmHDx9WXl6em8sDADBseRzHcdxa7OKnrP/2t7/JcRw98cQTys3NdWt5AACGLVeDDAAA\nPhsuDAIAgAEEGQAAAxIyyP39/aqurlZ5ebkqKirU1dV1yf0vv/yy5s+fr/LycjU2NsZpl7ZFm+He\nvXu1YMECLVq0SNXV1erv74/TTu2KNsOL1qxZo02bNg3x7hJDtBkePXpUfr9fixcv1kMPPaRwOByn\nndoVbYa7d+/WvHnzNH/+fG3fvj1Ou0wMR44cUUVFxUduH7KmOAnoD3/4g7NixQrHcRzntddec5Yu\nXTpwX29vrzN79mzn7NmzTjgcdsrKypzu7u54bdWsSDM8f/68M2vWLOfcuXOO4zhOVVWVs2/fvrjs\n07JIM7xox44dzsKFC52NGzcO9fYSQqQZ9vf3O9/+9redt99+23Ecx2lsbHQ6Ozvjsk/Lov05LC4u\ndnp6epxwODzw/0Z81NatW53bb7/dWbBgwSW3D2VTEvIdcqQrgnV2dio7O1tXXXWVvF6vvvrVr+ov\nf/lLvLZqVqQZer1eNTQ06IorrpAk9fX1KS0tLS77tCzalekOHTqkI0eOqLy8PB7bSwiRZvjWW29p\n9OjR+vWvf627775bZ8+eVU5OTry2ala0P4c33HCDAoGAent75TiOPB5PPLZpXnZ2tjZv3vyR24ey\nKQkZ5I+7ItjF+3y+/11VJT09XcFgcMj3aF2kGSYlJelzn/ucJKm+vl7nzp1TcXFxXPZpWaQZvvPO\nO/rZz36m6urqeG0vIUSaYU9Pj1577TXdfffd+tWvfqU///nPeuWVV+K1VbMizVCSJkyYoPnz5+tb\n3/qWZsyYoczMzHhs07zS0tLLXshqKJuSkEGOdEWw/78vFApdMkz8V7SrqvX396u2tlatra3avHkz\nv1VfRqQZ/v73v1dPT48eeOABbd26VXv37tWuXbvitVWzIs1w9OjRGj9+vHJzc5Wamqrp06dzffzL\niDTDjo4OHThwQM3NzXr55Zf17rvv6ne/+128tpqQhrIpCRnkSFcEy83NVVdXl86ePave3l69+uqr\nKiwsjNdWzYp2VbXq6mqFw2Ft2bJl4NQ1LhVphvfcc4927dql+vp6PfDAA7r99ttVVlYWr62aFWmG\nX/jCFxQKhQY+pPTqq69qwoQJcdmnZZFm6PP5NGrUKKWlpSk5OVljx47Ve++9F6+tJqShbEpCXmh6\nzpw5am1t1aJFiwauCLZnzx6dO3dO5eXlWrlype677z45jqP58+frmmuuifeWzYk0w0mTJumFF15Q\nUVGRlixZIum/gZkzZ06cd21LtD+HiC7aDNevX6/ly5fLcRwVFhZqxowZ8d6yOdFmWF5eLr/fr9TU\nVGVnZ2vevHnx3nJCiEdTuFIXAAAGJOQpawAAhhuCDACAAQQZAAADCDIAAAYQZAAADEjIf/YEjBQ7\nduzQjh071NfXJ4/Ho4kTJ6qqqkrXXnttxOetXr1aixYt0qRJk4ZopwBixTtkwKja2lq99NJLevbZ\nZ/Xb3/5We/bsUXFxscrLy3Xq1KmIzz148KD4F41AYuHfIQMGnTp1St/85jd14MABXXXVVZfct27d\nOl24cEF/+tOf9PTTT2vy5MmSpFtvvVVPP/209u3bp23btmncuHHasGGDrr32WtXU1OjNN99UUlKS\nFi1apHvuuUenTp3S2rVrdfLkSTmOozvuuEPf/e53deLECS1ZskRf+9rXdPjwYfX19emHP/yhdu7c\nqTfffFOTJk1SXV2dkpKSdOjQIW3atEnnz5+Xx+PRD37wA82cOTMeIwMSHqesAYOOHDminJycj8RY\nkm6++Wb99Kc//djnVlVVac+ePdq0aZMmT56s73//+/riF7+oLVu2KBAIaPHixbrlllv0+OOPa9as\nWfrOd76jQCCgu+66S5///Oc1ZcoUnThxQrfeeqvWr1+vmpoarV+/Xrt371ZqaqpmzZqlw4cPKzc3\nV6tWrdK2bdt03XXX6fTp01q4cKFuuOGGqKfUAXwUQQaM+vA39nxYb2/vp/qyj4MHD+rRRx+V9N9r\nG+/du1fnzp3ToUOH9Mtf/nLg9rKyMrW0tGjKlClKTU3VrbfeKum/X0tXWFg48I1CV199tf7973/r\n8OHD6u7u1oMPPjjwWh6PR2+88QZBBj4DggwYVFBQoK6uLnV3dysrK+uS+9ra2lRYWKiWlpZL/p64\nt7f3smulpKRcEvC///3vGj169Ef+jrm/v3/gl4DU1NRLnpOamvqRdS9cuKDc3Fw9//zzA7edPn1a\nY8eO/RRHCuAiPtQFGHTNNdeooqJCDz/8sE6fPj1we1NTk1566SXdf//9Gjt27MDXEV58t3rRh78T\n9+tf/7qampokSYFAQEuWLFFXV5emTJmi5557buD2F198UTfffPMn3uPFXxoufln766+/rtLSUr3z\nzjuxHTwwQvEOGTBq+fLlev7557Vs2TL19vaqt7dXkydPVkNDg8aNG6dHHnlEa9eu1c6dO3XTTTfp\npptuGnju7NmzVVVVpXXr1qm6ulpr167V3Llz5TiOvve972nSpEnatGmTfvzjH2vXrl3q7e3V3Llz\nVVZWppMnT36i/Y0dO1bPPPOMNmzYoHA4LMdxtGHDBo0bN26wRgIMa3zKGgAAAzhlDQCAAQQZAAAD\nCDIAAAYQZAAADCDIAAAYQJABADCAIAMAYABBBgDAgP8A3aatfgOkgcEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1194aa8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 看一下各个数据的分布柱状图\n",
    "print(data.columns.values)\n",
    "for column in data.columns.values :\n",
    "    zero = (data[column].values == 0).sum()\n",
    "    print \"【zero】统计:\", column,zero\n",
    "    fig = pyplot.figure()\n",
    "    sns.distplot(data[column].values, bins=20, kde=False)\n",
    "    pyplot.xlabel(column, fontsize=12)\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyEAAAILCAYAAAD/rpMXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCGEEGJaySRECCGEEEIIMa3+B3o02lX7kq+bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1194bd710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 看数据的相关性\n",
    "data_corr = data.corr().abs()\n",
    "pyplot.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data.drop(\"Outcome\",axis=1)\n",
    "y_train = data[\"Outcome\"]\n",
    "\n",
    "ss_X = StandardScaler()\n",
    "X_train_trans = ss_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试数据随机拆分 20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.008773</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.006251</td>\n",
       "      <td>0.005477</td>\n",
       "      <td>0.020295</td>\n",
       "      <td>0.027173</td>\n",
       "      <td>-0.016326</td>\n",
       "      <td>0.030085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.022645</td>\n",
       "      <td>0.995554</td>\n",
       "      <td>1.010737</td>\n",
       "      <td>1.006944</td>\n",
       "      <td>1.020167</td>\n",
       "      <td>1.004474</td>\n",
       "      <td>1.006313</td>\n",
       "      <td>1.022429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>-3.783654</td>\n",
       "      <td>-3.572597</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-4.060474</td>\n",
       "      <td>-1.189553</td>\n",
       "      <td>-1.041549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-0.685236</td>\n",
       "      <td>-0.367337</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.570195</td>\n",
       "      <td>-0.706334</td>\n",
       "      <td>-0.786286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.250952</td>\n",
       "      <td>-0.106239</td>\n",
       "      <td>0.149641</td>\n",
       "      <td>0.154533</td>\n",
       "      <td>-0.371623</td>\n",
       "      <td>0.051710</td>\n",
       "      <td>-0.310699</td>\n",
       "      <td>-0.360847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.629244</td>\n",
       "      <td>0.563223</td>\n",
       "      <td>0.766132</td>\n",
       "      <td>0.435886</td>\n",
       "      <td>0.606981</td>\n",
       "      <td>0.406580</td>\n",
       "      <td>0.660206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.906578</td>\n",
       "      <td>2.413181</td>\n",
       "      <td>2.734528</td>\n",
       "      <td>4.921866</td>\n",
       "      <td>6.652839</td>\n",
       "      <td>4.455807</td>\n",
       "      <td>5.883565</td>\n",
       "      <td>4.063716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   614.000000  614.000000     614.000000     614.000000  614.000000   \n",
       "mean      0.008773    0.000039       0.006251       0.005477    0.020295   \n",
       "std       1.022645    0.995554       1.010737       1.006944    1.020167   \n",
       "min      -1.141852   -3.783654      -3.572597      -1.288212   -0.692891   \n",
       "25%      -0.844885   -0.685236      -0.367337      -1.288212   -0.692891   \n",
       "50%      -0.250952   -0.106239       0.149641       0.154533   -0.371623   \n",
       "75%       0.639947    0.629244       0.563223       0.766132    0.435886   \n",
       "max       3.906578    2.413181       2.734528       4.921866    6.652839   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age  \n",
       "count  614.000000                614.000000  614.000000  \n",
       "mean     0.027173                 -0.016326    0.030085  \n",
       "std      1.004474                  1.006313    1.022429  \n",
       "min     -4.060474                 -1.189553   -1.041549  \n",
       "25%     -0.570195                 -0.706334   -0.786286  \n",
       "50%      0.051710                 -0.310699   -0.360847  \n",
       "75%      0.606981                  0.406580    0.660206  \n",
       "max      4.455807                  5.883565    4.063716  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 拆分 20% 数据作为测试集\n",
    "X_train_part, X_test, y_train_part, y_test = train_test_split(X_train_trans, y_train, train_size = 0.8,random_state = 0)\n",
    "\n",
    "## turn back to DataFrame\n",
    "X_train_part = pd.DataFrame(data=X_train_part, columns=X_train.columns)\n",
    "X_test_part = pd.DataFrame(data=X_test, columns=X_train.columns)\n",
    "\n",
    "X_train_part.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.034977</td>\n",
       "      <td>-0.000154</td>\n",
       "      <td>-0.024923</td>\n",
       "      <td>-0.021839</td>\n",
       "      <td>-0.080916</td>\n",
       "      <td>-0.108340</td>\n",
       "      <td>0.065090</td>\n",
       "      <td>-0.119950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.909958</td>\n",
       "      <td>1.024022</td>\n",
       "      <td>0.962180</td>\n",
       "      <td>0.978077</td>\n",
       "      <td>0.917387</td>\n",
       "      <td>0.981002</td>\n",
       "      <td>0.978258</td>\n",
       "      <td>0.901787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>-3.783654</td>\n",
       "      <td>-3.572597</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-4.060474</td>\n",
       "      <td>-1.123111</td>\n",
       "      <td>-1.041549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-0.638291</td>\n",
       "      <td>-0.263941</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.693941</td>\n",
       "      <td>-0.645177</td>\n",
       "      <td>-0.786286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.250952</td>\n",
       "      <td>-0.200131</td>\n",
       "      <td>0.097943</td>\n",
       "      <td>0.091805</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.100593</td>\n",
       "      <td>-0.268417</td>\n",
       "      <td>-0.445935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.342981</td>\n",
       "      <td>0.527528</td>\n",
       "      <td>0.537374</td>\n",
       "      <td>0.719086</td>\n",
       "      <td>0.305642</td>\n",
       "      <td>0.464197</td>\n",
       "      <td>0.599112</td>\n",
       "      <td>0.404942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.718712</td>\n",
       "      <td>2.444478</td>\n",
       "      <td>2.010760</td>\n",
       "      <td>1.848191</td>\n",
       "      <td>4.334506</td>\n",
       "      <td>2.653554</td>\n",
       "      <td>3.706059</td>\n",
       "      <td>2.702312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   154.000000  154.000000     154.000000     154.000000  154.000000   \n",
       "mean     -0.034977   -0.000154      -0.024923      -0.021839   -0.080916   \n",
       "std       0.909958    1.024022       0.962180       0.978077    0.917387   \n",
       "min      -1.141852   -3.783654      -3.572597      -1.288212   -0.692891   \n",
       "25%      -0.844885   -0.638291      -0.263941      -1.288212   -0.692891   \n",
       "50%      -0.250952   -0.200131       0.097943       0.091805   -0.692891   \n",
       "75%       0.342981    0.527528       0.537374       0.719086    0.305642   \n",
       "max       2.718712    2.444478       2.010760       1.848191    4.334506   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age  \n",
       "count  154.000000                154.000000  154.000000  \n",
       "mean    -0.108340                  0.065090   -0.119950  \n",
       "std      0.981002                  0.978258    0.901787  \n",
       "min     -4.060474                 -1.123111   -1.041549  \n",
       "25%     -0.693941                 -0.645177   -0.786286  \n",
       "50%     -0.100593                 -0.268417   -0.445935  \n",
       "75%      0.464197                  0.599112    0.404942  \n",
       "max      2.653554                  3.706059    2.702312  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_part.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wuzhong/anaconda3/envs/py27/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/Users/wuzhong/anaconda3/envs/py27/lib/python2.7/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# 引入包\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.cross_validation import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression\n",
      "logloss of each fold is:  [ 0.50638103  0.45402081  0.472463    0.58508054  0.46964588]\n",
      "cv logloss is: 0.497518252913\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "loss = cross_val_score(lr, X_train_part, y=y_train_part, scoring=\"neg_log_loss\", cv=5)\n",
    "print(\"LogisticRegression\")\n",
    "print 'logloss of each fold is: ', -loss\n",
    "print'cv logloss is:', -loss.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV\n",
      "0.49743547256\n",
      "('best parmas is :', {'penalty': 'l2', 'C': 1})\n",
      "Classification report for classifier GridSearchCV(cv=5, error_score='raise',\n",
      "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False),\n",
      "       fit_params={}, iid=True, n_jobs=1,\n",
      "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
      "       pre_dispatch='2*n_jobs', refit=True, scoring='neg_log_loss',\n",
      "       verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.92      0.88       107\n",
      "          1       0.76      0.62      0.68        47\n",
      "\n",
      "avg / total       0.82      0.82      0.82       154\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[98  9]\n",
      " [18 29]]\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "\n",
    "penaltys = [\"l1\", \"l2\"]\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "map = dict(penalty=penaltys, C=Cs)\n",
    "lr_penalty = LogisticRegression()\n",
    "grid = GridSearchCV(lr_penalty, map, cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train_part, y_train_part)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 最佳参数查找"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV\n",
      "0.49743547256\n",
      "('best parmas is :', {'penalty': 'l2', 'C': 1})\n"
     ]
    }
   ],
   "source": [
    "print(\"GridSearchCV\")\n",
    "print(-grid.best_score_)\n",
    "print(\"best parmas is :\",grid.best_params_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier GridSearchCV(cv=5, error_score='raise',\n",
      "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False),\n",
      "       fit_params={}, iid=True, n_jobs=1,\n",
      "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
      "       pre_dispatch='2*n_jobs', refit=True, scoring='neg_log_loss',\n",
      "       verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.92      0.88       107\n",
      "          1       0.76      0.62      0.68        47\n",
      "\n",
      "avg / total       0.82      0.82      0.82       154\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[98  9]\n",
      " [18 29]]\n"
     ]
    }
   ],
   "source": [
    "y_predict = grid.predict(X_test_part)\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (grid, metrics.classification_report(y_test, y_predict)))\n",
    "print(\"Confusion matrix:\\n%s\" % metrics.confusion_matrix(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论\n",
    "\n",
    "1.  正则函数 l2\n",
    "2. 正则参数 1\n",
    "3. 测试结果 0.82"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC\n",
      "Classification report for classifier LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.92      0.88       107\n",
      "          1       0.76      0.62      0.68        47\n",
      "\n",
      "avg / total       0.82      0.82      0.82       154\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[98  9]\n",
      " [18 29]]\n",
      "score:\n",
      "0.824675324675\n"
     ]
    }
   ],
   "source": [
    "svc = LinearSVC().fit(X_train_part,y_train_part)\n",
    "y_predict = svc.predict(X_test_part)\n",
    "print(\"LinearSVC\")\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (svc, metrics.classification_report(y_test, y_predict)))\n",
    "print(\"Confusion matrix:\\n%s\" % metrics.confusion_matrix(y_test, y_predict))\n",
    "print(\"score:\\n%s\" % svc.score(X_test_part, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_grid_point_Linear(C, X_train, y_train, X_val, y_val):\n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC2 = LinearSVC(C=C)\n",
    "    SVC2 = SVC2.fit(X_train, y_train)\n",
    "\n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC2.score(X_val, y_val)\n",
    "\n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.785714285714\n",
      "accuracy: 0.824675324675\n",
      "accuracy: 0.824675324675\n",
      "accuracy: 0.824675324675\n",
      "accuracy: 0.824675324675\n",
      "accuracy: 0.720779220779\n",
      "accuracy: 0.766233766234\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wuzhong/anaconda3/envs/py27/lib/python2.7/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFXCAYAAAC7nNf0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11960a410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 需要调优的参数\n",
    "C_s = np.logspace(-3, 3, 7)  # logspace(a,b,N)把10的a次方到10的b次方区间分成N份\n",
    "# penalty_s = ['l1','l2']\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    #    for j, penalty in enumerate(penalty_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, X_train_part, y_train_part, X_test_part, y_test)\n",
    "    accuracy_s.append(tmp)\n",
    "\n",
    "x_axis = np.log10(C_s)\n",
    "# for j, penalty in enumerate(penalty_s):\n",
    "pyplot.plot(x_axis, np.array(accuracy_s), 'b-')\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.xlabel('log(C)')\n",
    "pyplot.ylabel('accuracy')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论\n",
    "\n",
    "1.  正则函数 l2\n",
    "2. 正则参数 1\n",
    "3. 测试结果 0.825\n",
    "\n",
    "与 LogisticRegression 的性能基本一致\n",
    "\n",
    "#### 原因阐述\n",
    "\n",
    "1. 一般当训练样本较大时，选择Logistic Regression或者不带核函数的SVM；\n",
    "2. 当训练样本较少时，并且特征维度适中，选择带高斯核的SVM;\n",
    "3. 当训练样本较少，并且特征维度也少，尽量再增加些特征，然后选择Logistic Regression或者不带核函数的SVM，当前数据为此场景\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RBF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_grid_point_RBF(C, gamma, X_train, y_train, X_val, y_val):\n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC3 = SVC(C=C, kernel='rbf', gamma=gamma)\n",
    "    SVC3 = SVC3.fit(X_train, y_train)\n",
    "\n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC3.score(X_val, y_val)\n",
    "\n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.805194805195\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.824675324675\n",
      "accuracy: 0.779220779221\n",
      "accuracy: 0.75974025974\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.818181818182\n",
      "accuracy: 0.766233766234\n",
      "accuracy: 0.707792207792\n",
      "accuracy: 0.688311688312\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.792207792208\n",
      "accuracy: 0.753246753247\n",
      "accuracy: 0.707792207792\n",
      "accuracy: 0.688311688312\n",
      "accuracy: 0.694805194805\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFXCAYAAAC7nNf0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\neAlJ6kIIIYSXkKQuhBBCeIn/D4fWRlUYW9AXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115cc5d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-2, 2, 5)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份\n",
    "gamma_s = np.logspace(-2, 2, 5)\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train_part, y_train_part, X_test_part, y_test)\n",
    "        accuracy_s.append(tmp)\n",
    "\n",
    "accuracy_s1 =np.array(accuracy_s).reshape(len(C_s),len(gamma_s))\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    pyplot.plot(x_axis, np.array(accuracy_s1[:,j]), label = ' Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )\n",
    "pyplot.ylabel( 'accuracy' )\n",
    "# pyplot.savefig('RBF_SVM_Otto.png' )\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论\n",
    "\n",
    "1. 正则参数 0\n",
    "2. 核函数宽度 -2\n",
    "3. 测试结果 0.825\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
